doi:10.1093/brain/awr331
Brain 2012: 135; 596–614
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BRAIN
A JOURNAL OF NEUROLOGY
Parietofrontal integrity determines neural
modulation associated with grasping
imagery after stroke
Ethan R. Buch,1,2 Amirali Modir Shanechi,1,3 Alissa D. Fourkas,1 Cornelia Weber,4
Niels Birbaumer4,5 and Leonardo G. Cohen1
1
2
3
4
5
Human Cortical Physiology and Stroke Neurorehabilitation Section, NINDS, NIH, Bethesda, MD 20892, USA
Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK
Washington University School of Medicine, St Louis, MO 63110, USA
Institute of Medical Psychology and Behavioural Neurobiology, Eberhard-Karls-University of Tübingen, D-72074 Tübingen, Germany
Ospedale San Camillo, IRCCS, Venice, Italy
Correspondence to: Ethan R. Buch or Leornard G. Cohen,
Building 10, Room 7D54
MSC 1428 10 Center Drive Bethesda,
MD 20892-1428, USA
E-mail: [email protected] or [email protected]
Chronic stroke patients with heterogeneous lesions, but no direct damage to the primary sensorimotor cortex, are capable of
longitudinally acquiring the ability to modulate sensorimotor rhythms using grasping imagery of the affected hand. Volitional
modulation of neural activity can be used to drive grasping functions of the paralyzed hand through a brain–computer interface.
The neural substrates underlying this skill are not known. Here, we investigated the impact of individual patient’s lesion
pathology on functional and structural network integrity related to this volitional skill. Magnetoencephalography data acquired
throughout training was used to derive functional networks. Structural network models and local estimates of extralesional
white matter microstructure were constructed using T1-weighted and diffusion-weighted magnetic resonance imaging data. We
employed a graph theoretical approach to characterize emergent properties of distributed interactions between nodal brain
regions of these networks. We report that interindividual variability in patients’ lesions led to differential impairment of functional and structural network characteristics related to successful post-training sensorimotor rhythm modulation skill. Patients
displaying greater magnetoencephalography global cost-efficiency, a measure of information integration within the distributed
functional network, achieved greater levels of skill. Analysis of lesion damage to structural network connectivity revealed that
the impact on nodal betweenness centrality of the ipsilesional primary motor cortex, a measure that characterizes the importance
of a brain region for integrating visuomotor information between frontal and parietal cortical regions and related thalamic nuclei,
correlated with skill. Edge betweenness centrality, an analogous measure, which assesses the role of specific white matter fibre
pathways in network integration, showed a similar relationship between skill and a portion of the ipsilesional superior longitudinal fascicle connecting premotor and posterior parietal visuomotor regions known to be crucially involved in normal grasping behaviour. Finally, estimated white matter microstructure integrity in regions of the contralesional superior longitudinal
fascicle adjacent to primary sensorimotor and posterior parietal cortex, as well as grey matter volume co-localized to these
specific regions, positively correlated with sensorimotor rhythm modulation leading to successful brain–computer interface
control. Thus, volitional modulation of ipsilesional neural activity leading to control of paralyzed hand grasping function through
a brain–computer interface after longitudinal training relies on structural and functional connectivity in both ipsilesional and
contralesional parietofrontal pathways involved in visuomotor information processing. Extant integrity of this structural network
Received July 24, 2011. Revised October 3, 2011. Accepted October 19, 2011. Advance Access publication January 9, 2012
ß The Author (2012). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
For Permissions, please email: [email protected]
Parietofrontal integrity after stroke
Brain 2012: 135; 596–614
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may serve as a future predictor of response to longitudinal therapeutic interventions geared towards training sensorimotor
rhythms in the lesioned brain, secondarily improving grasping function through brain–computer interface applications.
Keywords: motor learning; skill; stroke; rehabilitation; brain–computer interface; imagery
Introduction
Chronic stroke is a leading cause of long term motor disability
among adults, as rehabilitative interventions are largely unsuccessful in improving the most severe cases of motor impairment following the event (Lai et al., 2002). This lack of recovery is
particularly exacerbated in the case of hand function (Krakauer,
2005). The human brain selects and executes reaching and grasping actions through the dynamic linkage of specific neuron populations into functional networks (Shadmehr and Wise, 2005).
These networks are continuously sculpted through modifications
of underlying anatomical microstructure, which optimize and buttress advantageous interaction patterns through experience-based
plasticity mechanisms triggered by evaluation of contingent goals,
actions and sensory consequences (Johansen-Berg, 2007;
Holtmaat and Svoboda, 2009; Wilbrecht et al., 2010). When
the brain suffers a substantial injury, such as following stroke, a
proportion of the anatomical components of these networks may
be compromised and result in radically perturbed functional dynamics (Honey and Sporns, 2008; Alstott et al., 2009). In a finite
acute window following the event, a state of synaptic instability
occurs within peri-infarct regions that allows for rapid reorganization of constituent networks (Dancause et al., 2006; Brown et al.,
2007; Murphy and Corbett, 2009; Dimyan and Cohen, 2011;
Yu and Zuo, 2011). Although plasticity within these networks
may attenuate once the subacute phase is reached, a considerable
capacity for reorganization is still maintained that continues
throughout the chronic phase, as well (Ward, 2005; Nudo,
2006; Sawaki et al., 2008; Murphy and Corbett, 2009).
It has been repeatedly shown that physical training in acute,
subacute and chronic stages of stroke is capable of influencing
compensatory functional network interaction patterns and underlying anatomical microstructure (Nudo, 2006). This is the scientific
basis for a majority of novel rehabilitation strategies that are progressively used in clinical settings, including constraint-induced
movement therapy (Taub et al., 1993; Wolf et al., 2010) or
active forms of robotic-assisted upper limb therapy, in which the
robot amplifies intrinsic muscle forces produced by the patient to
assist in the completion of a visuomotor task (Fasoli et al., 2003;
Lo et al., 2010). A major limitation to these types of interventions,
however, is that they fail to be inclusive of patients with severe
hand function deficits, as their implementation relies on the presence of some residual motor function. In many clinical trials, this
has meant the exclusion of up to 80% of otherwise eligible patients (Grotta et al., 2004; Sawaki et al., 2008). In patients experiencing severe paralysis, contingencies between overt actions and
consequences can no longer be used to drive reorganization within
functional brain networks, making them prone to devolution
towards a maladaptive state indicative of learned disuse
(Krakauer, 2006; Pomeroy et al., 2011). Several recent studies
suggest that functional network organization may be impacted
through operant conditioning paradigms that use neurofeedback
to establish new contingencies between volitional modulation of
neural activity and extrinsic sensory feedback (Jarosiewicz et al.,
2008; Legenstein et al., 2010). Thus, interventions that train patients to acquire volitional control of neural activity modulation
may be beneficial for this particular group of patients with
stroke (Grosse-Wentrup et al., 2011a).
Motor imagery is one strategy used to condition voluntary control of neural activity in visuomotor networks. Several neuroimaging and electrophysiological studies have observed that motor
imagery of grasping movements, the conscious rehearsal of egocentric motor actions without overt motor output (Jeannerod
et al., 1995), engages bilateral parietofrontal networks in a similar
manner to executed movements (McFarland et al., 2000;
Pfurtscheller, 2000; Sharma et al., 2008, 2009a, b; Gao et al.,
2010). As a result, motor imagery training has been investigated
as a means of actively engaging extant motor networks in chronic
stroke populations with severe hand function deficits (Sharma
et al., 2006; Page et al., 2007, 2009). More recently, augmentation of motor imagery training with brain–computer interface
technology, which implements exogenous contingencies between
neural activity and sensory feedback, has been promoted as a
possible strategy for increasing the efficacy of this type of rehabilitation through the establishment of explicit links between neural
activity modulation related to covert motor intentions, and sensory
consequences (Daly and Wolpaw, 2008; Wang et al., 2010b).
Additionally, brain–computer interface approaches can provide
direct brain control of mechanical orthoses, robotic exoskeleton
devices or functional electrical stimulation systems interfaced
with the impaired limb leading to restoration of function
(Birbaumer and Cohen, 2007). To that end, we have previously
reported that a group of patients with chronic stroke with severe
hand paralysis can longitudinally learn to control a non-invasive
brain–computer interface that operates a mechanical hand orthosis
through volitional modulation of sensorimotor rhythms recorded
over the ipsilesional hemisphere (Buch et al., 2008). The neural
substrates underlying the ability of patients with stroke to successfully control volitional sensorimotor rhythm modulation through
motor imagery are not known. One approach to address this
question is to evaluate the impact of each individual patient’s
pathology on characteristics of engaged anatomical and functional
cortical networks.
In the past few years, both structural and functional connectivity
neuroimaging data have been increasingly modelled using an
applied form of graph theory known as complex network analysis
(Sporns, 2011). This analytical approach attempts to explicitly
characterize both the direct and indirect extrinsic contributions to
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E. R. Buch et al.
Table 1 Patient lesion characteristics
Patient
Age
(years)
Sex
Ipsilesional
hemisphere
Time post
stroke (m)
Total lesion
volume
(cm3)
Grey matter
proportion of
lesion
White matter
proportion of
lesion
MRC score of residual
finger flexion/extension
(out of 5)
SP1
SP2
SP3
SP4
SP5
SP6
SP7
SP8
Mean SD
63
64
53
55
53
67
44
68
58.38 8.42
F
F
M
M
M
M
F
M
Right
Right
Left
Right
Left
Right
Right
Right
30
69
25
41
12
14
24
37
31.50 18.19
80.00
25.39
57.16
14.93
7.41
4.10
17.57
64.10
33.83 28.95
0.15
0.17
0.17
0.16
0.19
0.26
0.05
0.18
0.17 0.06
0.83
0.83
0.81
0.83
0.78
0.74
0.95
0.80
0.82 0.06
0/0
1/0
0/0
1/0
0/0
0/0
0/0
1/0
0.37 0.52/0.00 0.00
F = female; M = male.
regional function in the brain (Passingham et al., 2002). Complex
network graphs and other methods that quantify interactions between brain regions have recently given new insight into the spontaneous reorganization of functional and structural brain networks
following stroke (Nomura et al., 2010; Wang et al., 2010a; Crofts
et al., 2011; Grefkes and Fink, 2011; Rehme et al., 2011b).
Several studies suggest that one property of these networks,
cost-efficiency, is an important optimization principle that governs
both structural and functional brain network architecture (Fornito
et al., 2011). Highly cost-efficient networks preferentially employ
long-range connectivity, allowing for faster and more robust information transfer between discrete brain regions, while at the
same time minimizing the related energy cost of fibre pathway
maintenance (Laughlin and Sejnowski, 2003; Buzsaki et al.,
2004; Achard and Bullmore, 2007). This property appears to
have behavioural implications as well, since it has been shown
to correlate with memory and intellectual performance in both
healthy volunteers and patient groups (Bassett et al., 2009; van
den Heuvel et al., 2009). A related local property of network
graphs, betweenness centrality, highlights specific brain regions
or white matter fibre pathways that are particularly important
for functional integration between distant brain areas (Rubinov
and Sporns, 2010), and has been shown to predict motor hand
recovery in stroke (Wang et al., 2010a). This property also reveals
locations within networks where functional dynamics are more
vulnerable to the impact of brain lesions (Honey and Sporns,
2008; Alstott et al., 2009). Exploration of these two properties
of brain networks may lead to more insight into relationships between abnormal brain activation patterns, and behavioural deficits
or recovery following stroke (Grefkes and Fink, 2011). Moreover,
as these properties are described within a topological framework
and can be related non-spatially, they hold a distinct advantage
over voxel-based techniques for characterizing pathological
changes in brain connectivity in heterogeneous stroke populations
(Crofts et al., 2011).
Here, we employed a combination of neuroimaging modalities,
including magnetoencephalography, and diffusion and T1weighted structural MRI, to evaluate the impact of each individual patient’s lesion pathology on their ability to modulate
ipsilesional sensorimotor rhythm power (using affected hand
motor imagery strategies) after longitudinal brain–computer interface training. For the first time, we use a complex network analytical approach to understand the neural substrates of voluntary
modulation of neural activity through operant conditioning after
chronic stroke. We hypothesized that the longitudinally acquired
skill of volitional neural activity modulation would be directly
related to extant parietofrontal structural network integrity facilitating communication between premotor, primary motor and posterior parietal visuomotor cortical regions normally involved in
grasping function.
Patients and methods
Patients
Patients with chronic hand plegia resulting from a single subcortical or
mixed (cortical and subcortical) stroke (n = 8; mean age = 58.4 8.4
years, mean duration = 31.5 18.2 months) were recruited from the
Human Cortical Physiology and Stroke Neurorehabilitation Section of
the National Institute for Neurological Disorders and Stroke (n = 6) and
the Clinic of Neurology of the University of Tübingen (n = 2). Patients
were included in the study if they had a history of a single stroke, and
expressed residual finger extension strength rated as 0/5 on the
Medical Research Council (MRC) scale (Table 1). All patients also
had either a 0/5 or 1/5 MRC score for finger flexion strength and
were incapable of voluntary finger extension in the plegic hand.
Shoulder, elbow and finger flexor and extensor spasticity was rated
as 43 on the Modified Ashworth Scale for all patients. These criteria
ensured that the affected arm could maintain a comfortable posture
while seated in the magnetoencephalography chair, and that movement of the affected hand could only be produced via passive manipulation from the attached orthosis. Cognitive function was assessed
using the Mini-Mental State Examination (Folstein et al., 1975), and
found to be in the normal range (all patients scored 523 out of 30).
Patients provided written informed consent and the study was
approved by the Institutional Review Board of the National Institute
of Neurological Disorders and Stroke and the Ethical Committee of the
Faculty of Medicine of the University of Tübingen. Individual patient
lesions and group probability lesion maps can be seen in Fig. 1.
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Figure 1 Brain lesions. (A) Sagittal, coronal and axial views of individual subject T1-weighted MRI scans with segmented brain lesions.
A secondary segmentation of the total lesion was implemented to distinguish between core (red) and peri-necrotic (blue) regions based on
differences in T1 signal intensity. Slices for each view are shown at the centre of gravity location for the core lesion. (B) Group core lesion
(top; slices through MNI coordinate x = 26, y = 15, z = 2), peri-necrotic lesion (middle; 24, 18, 35) and total lesion (bottom; 25, 15,
29) probability maps displayed in MNI152 space. Left hemisphere lesions for Patients SP3 and SP5 have been flipped to the right-side. Two
clusters (outlined in black) within the group lesion probability map show an overlap of at least seven of eight patients. The first cluster
(517 mm3 volume; centre of gravity: 23.16, 14.57, 29.23) is located within the superior portion of the corona radiata, while a smaller
more inferior cluster (60 mm3 volume; 25.38, 14.17, 2.87) is located in the external medullary lamina.
Experimental design
Brain–computer interface training sessions
Patients participated in an average of 15.4 4.8 brain–computer
interface training sessions (range 9–22) with an average frequency
of 4.6 0.8 sessions per week. Each training session lasted 1–2 h
and was implemented on an outpatient basis.
Magnetoencephalography recordings
Neuromagnetic activity recorded from a 275-channel (seven patients)
or 151-channel (one patient) magnetoencephalography array (CTF
MEGTM) was used to control a brain–computer interface as previously
described at both the National Institutes of Health and the University
of Tübingen. The magnetoencephalography apparatus was housed in
a magnetically shielded room and used synthetic third gradient
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balancing to actively reduce interference from environmental noise.
Recordings from all magnetoencephalography channels were antialiased with a 200-Hz cut-off, low-pass filter, and digitally sampled
at 600 Hz. During recording, patients were seated alone in the shielded
magnetoencephalography room with the lights slightly dimmed, and
their head centrally positioned within the sensor array.
Sensorimotor rhythm-based brain–computer interface
Harmonic brain rhythms are believed to be an important mechanism
underlying regional brain activity, as well as the long-range synchronization and dynamic recruitment of multiple distant brain regions into
global functional networks. Regularly observable brain rhythms span
several frequency bands and have been linked to important cognitive
and sensorimotor functions (Palva and Palva, 2011). One of these, the
sensorimotor or m-rhythm, is believed to play an important functional
role in transforming perceptual goals to motor actions (Pineda, 2005).
The sensorimotor rhythm has a base m band frequency range of
9–12 Hz, with a harmonic in the band at 20–24 Hz, and can be
recorded from scalp regions overlying the sensorimotor cortex. The
terms event-related synchronization and desynchronization are commonly used to describe increases and decreases, respectively, in sensorimotor rhythm power relative to a baseline. Sensorimotor rhythm
desynchronization has been repeatedly observed during the planning,
execution, or even imagination of limb movements (Pfurtscheller,
1977; McFarland et al., 2000), a particularly pertinent feature for
use in patients with chronic stroke with severe hand plegia. The
BCI2000 software platform (www.bci2000.org) was used to estimate
and classify sensorimotor rhythm state modulation, and drive the
task-related visual feedback display in real-time during brain–computer
interface training. Sensorimotor rhythm power estimates were derived
from a cluster of 3–4 magnetoencephalography sensors selected from
a subset of the array overlying the ipsilesional sensorimotor cortex, as
training of this cortical region would actively engage motor networks
affected by the stroke lesion. Surface EMG was recorded from extrinsic
hand muscles during training sessions.
Real-time feedback and brain–computer interface
training task
Patients performed up to 250 trials of a goal-oriented, onedimensional visual feedback task per training session. The task was
designed to help patients achieve volitional control of sensorimotor
rhythm modulation overlying the ipsilesional sensorimotor cortex,
and thus control of the orthosis (Fig. 2). As the task corresponded
to the two possible orthosis states (opened or closed grasping posture
of the affected hand), individual trials were initiated by the presentation of a visual target on either the upper or lower half of the right
side of a monitor display. Each target was essentially a visual representation of the sensorimotor rhythm power range assigned to the
corresponding orthosis action by an adaptive classifier. A square
screen cursor would then begin moving at a fixed rate from
left-to-right across the display with the cursor feedback updated
every 200 ms. The vertical height of the cursor was a transformation
of the difference between the current sensorimotor rhythm state estimate and an adaptive baseline set by the brain–computer interface
classifier. The goal for the patient was to modulate sensorimotor
rhythm power in the direction needed (above or below the baseline)
to deflect the screen cursor towards the target, and ultimately contact
the target when the cursor reached the right edge of the screen. The
brain–computer interface software maintained a finite history of the
mean sensorimotor rhythm power estimate from each trial and assigned this to a distribution representing observations for each target
E. R. Buch et al.
(or orthosis action) state. The baseline, defined as the midpoint between the means of these two distributions, was adaptive to account
for changes in these sensorimotor rhythm power distributions over the
course of training. At the conclusion of each successful trial (cursor
contact with target), a simultaneous change in target colour (red to
yellow) and orthosis action occurred, providing reinforcement. If the
cursor failed to hit the target, no reinforcement (target colour change
or orthosis action) was provided.
Hand orthosis
During all brain–computer interface training sessions, a mechanical
orthosis was attached to the plegic hand. Fingers 2–5 (index,
middle, ring and little fingers) were individually inserted into ring-like
fasteners that fixed each digit at the first phalanx. Each fastener was
connected to a plastic Bowden cable. Computer-gated pneumatic
valves extended or retracted the cables to manipulate hand grasping
posture. The orthosis had two possible movement motions: synchronous flexion or extension of the fingers resulting in hand grasp closing
or opening, respectively. The hand posture limits produced by these
two orthosis states were customized to each patient, so that they
manipulated the hand through a safe range-of-motion. On successful
trials, orthosis action triggers were synchronized in time with target
colour changes in the brain–computer interface training task through a
custom control circuit.
Magnetic resonance imaging data
acquisition
Whole brain, single-shot echo-planar (EPI) diffusion-weighted volumes
(110 non-collinear directions; b = 100 smm 2 [10 directions],
300 mm 2 [10], 500 smm 2 [10], 800 smm 2 [30] or 1100 smm 2
[50]; 60 slices; voxel size 2.5 2.5 2.5 mm3; echo time/repetition
time = 76.4 ms/18.28 s) plus 10 volumes without diffusion weighting
(b = 0 smm 2) were acquired for seven of the eight subjects
(excluding Patient SP6) following training on a 3.0 Tesla GE Excite
scanner using an 8-channel coil (GE Medical Systems). In addition
structural T1-weighted (magnetization-prepared rapid gradient-echo
sequence; echo time/repetition time = 2.67 ms/6.26 s, flip angle = 12 ;
voxel size = 0.9375 0.9375 1 mm3) and T2-weighted (echo time/
repetition time = 122.52 ms/8.35 s; voxel size = 0.4688 0.4688 1.5 mm3) volumes were acquired for all subjects.
Data analysis
Sensorimotor rhythm modulation skill
The success rate, or the proportion of trials in which patients were
successful at producing the required sensorimotor rhythm power
modulation that resulted in screen cursor contact with the target,
was computed for trial presentations during a single training session
and used as a skill measure. As the task was one-dimensional and
consisted of only two states, initial success rates of 0.5 indicate
‘chance’ levels of performance. A paired t-test was used to compare
changes in group skill (mean of first three and final three sessions)
following training. To assess if individual patients showed significant
changes in skill during training, the change-point test was used (Siegel
and Castellan, 1988). The change-point test assumes the null hypothesis that no time trend exists in the series of performance data. Based
on this assumption, skill for each session should rank on average near
the median, and the cumulative sum of ranks should increase approximately linearly with session number. The maximal deviation from this
expected linear increase in rank is considered as a potential
Parietofrontal integrity after stroke
Brain 2012: 135; 596–614
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Figure 2 Trial description for sensorimotor rhythm (SMR) modulation through grasping imagery training. Whole-head magnetoencephalography data were continuously recorded throughout each training block (48 trials). At the initiation of each trial, one of two targets
(top-right or bottom-right edge of visual display) appeared on a projection screen positioned in front of the subject. A screen cursor
appeared at the left edge of the screen 500 ms later, and began moving towards the right edge at a fixed rate. Sensorimotor rhythm power
modulation was estimated from a preselected subset of the sensor array (three to four source sensors) at 150-ms intervals throughout the
trial (4 s duration), and compared to an adaptive baseline that characterized the midpoint between the power distribution means for each
task state (target/orthosis action conditions). The distance of the current power state from this baseline was transformed into an upwards
(positive) or downwards (negative) deflection of the screen cursor’s vertical position, with an update rate of 6.7 Hz. At the conclusion of
the trial, if the subject successfully deflected the cursor to contact the target, two simultaneous reinforcement events occurred. The cursor
and target on the display changed colours from red to yellow, and the orthosis manipulated the impaired hand’s grasp posture to the
alternative state (opening or closing of hand). If the cursor did not successfully contact the target, no orthosis action was initiated.
‘change-point’ and is used to divide the time series into two components. These components are then compared using a Kolmogorov–
Smirnov test to determine statistical significance at = 0.5.
Magnetoencephalography data
Preprocessing and analysis of the data was performed using the
Fieldtrip toolbox for EEG/magnetoencephalography analysis, developed at the Donders Institute for Brain, Cognition and Behavior
(http://www.ru.nl/neuroimaging/fieldtrip).
A
Discrete
Fourier
Transform (DFT) filter was used to remove line noise from the raw
data (60 Hz for the six NIH patients, and 50 Hz for the two patients
from the University of Tübingen). The data was then band-pass filtered between 0.1–50 Hz, and finally down-sampled to 120 Hz to
reduce offline processing loads.
Raw signals from CTF axial gradiometers have contributions from a
wide spatial range of sources inside the brain, making it difficult to
interpret signal topography in sensor space. Several common source
regression modelling techniques, such as beamformers, have been developed to estimate signal source locations within the brain volume,
but in doing so change the inherent covariance structure of the raw
time-series data that is used to derive functional connectivity estimates. Thus, to retain the time-series correlation structure of the
raw sensor space data, and make spatial features of the sensor
space data easier to interpret, we performed a planar gradient transformation of that data at each sensor (Bastiaansen and Knösche,
2000). All further analyses were performed on planar gradient data.
The average functional connectivity between each sensor pair for
each task state was estimated by computing the phase-locking value.
If the time-series of each trial for a particular sensor and task state is
denoted as xn(t) for n = 1 to N, where N is the number of trials for
that particular task state, a complex time–frequency power spectral
density representation Xn(f, t) for xn(t), can be calculated by implementing Welch’s averaged modified periodogram method with a sliding Hamming window of 1 s duration and 50% overlap between
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adjacent windows. Similarly, the time–frequency representation,
Yn(f, t), can be calculated for the time-series recorded at a second
sensor in the array, yn(t), and the phase-locking value (PLV) between
the two sensors for that task state can be defined as:
! N
1 X
Xn ðf,tÞ
Yn ðf,tÞ PLVxy ðf,tÞ ¼ N n¼1 jXn ðf,tÞj Yn ðf,tÞ Phase-locking value quantifies the across-trial consistency of the
phase relationship between two sensors as a function of frequency
and time. Phase-locking values were calculated for all pair-wise
sensor combinations for each of the two brain–computer interface
task states during the feedback period (during which the patients
were actively trying to deflect the screen cursor towards the target),
and the mean phase-locking value was determined for the same frequency band from which sensorimotor rhythm state estimates were
derived (12 Hz for six patients, 12.5 Hz for one patient, and 9 Hz for
one patient). All magnetoencephalography measures were calculated
for each session of training (either sensorimotor rhythm modulation or
cost-efficiency). Since the number of sessions was slightly different
between our patients, we then resampled each magnetoencephalography measure versus session curve to have 20 sessions. The final magnetoencephalography and behavioural measures included in the
regression analysis were the average of the last three sessions taken
from this resampled curve.
Functional network construction
Phase-locking value estimates for every sensor pair were used to construct a functional connectivity matrix, Wij, which represents the phase
coupling between sensors i and j. The values of the main diagonal of
the matrix are set to zero, Wii = 0, so that there are no selfconnections. The connectivity matrix can then be thresholded and
binarized to construct an undirected binary graph, G:
1, if Wi,j4T
Gi,j ¼
0, otherwise
where T is a threshold that determines the sparseness of edges (or
connections between sensor pairs) in the resulting graph.
Following the approach of Basset et al. (2009), we examined the
relationship between functional network cost-efficiency observed for
individual patients and their sensorimotor rhythm modulation skill
(Bassett et al., 2009). For each threshold, T, applied to create the
network graph, G, consisting of N nodes, the cost or connection density, C, is defined as:
C¼
X
1
Gi,j
NðN 1Þ i6¼j
The regional efficiency at each sensor, E(i), is defined as:
E ði Þ ¼
1 X 1
N 1 j Li,j
where Li,j is the minimum number of edges that connect sensor i to j,
and N is again the total number of nodes in the network. Note that if
no connection exists between i and j, then Li,j = 1 and 1/Lij = 0. The
global efficiency, E, is the mean of E(i) over all sensors. Thus, for each
threshold, T, the cost-efficiency, CE, is defined as:
CE ¼ E C
In general, for a given fixed number of connections (cost), a greater
proportion of long-range connections results in higher global
cost-efficiency as information can be shared between distant network
nodes via shorter paths. One of the challenges faced when deriving a
binary association matrix from a continuous connectivity measure, is
E. R. Buch et al.
determining how to select the binarization threshold in a non-arbitrary
way. Furthermore, determining a single threshold that can be applied
across a group of patients in an unbiased manner can also be difficult,
as the connection density must be controlled to make meaningful
comparisons between different network graphs. In this case, this was
particularly important as the goal of the comparison was to assess how
differences in the pattern of connections (as opposed to the number of
connections) related to sensorimotor rhythm skill.
The major assumption of this method, that network architecture is
organized in a manner that optimizes the relationship between global
efficiency (a quality of how information is integrated across the brain)
and cost (the number of connections maintained to share that information) has been recently supported by functional imaging studies
(Achard and Bullmore, 2007; Fornito et al., 2011). To determine the
optimal cost-efficient functional network architecture observed in each
individual patient, we derived a series of binary, undirected networks
that were defined over a range of fixed costs. For individual graphs
possessing a specific cost, a threshold, T, was used that was determined by the phase-locking value distribution for the functional connectivity matrix calculated for each patient. The maximum
cost-efficiency value was calculated for data acquired over the final
three training sessions for each patient and then regressed against
sensorimotor rhythm modulation skill from the same period, providing
the most unbiased approach for assessing the relationship between
these two variables. The relationship between this network measure
and sensorimotor rhythm modulation skill was statistically assessed
using iteratively re-weighted-least squares regression (Street et al.,
1988).
This magnetoencephalography data analysis allows for the comprehensive exploration of global functional network organization after
stroke, our focus of interest. On the other hand, while alternative
methodologies involving source-based connectivity modelling of magnetoencephalography data (i.e. dynamic causal model) allow for more
direct interpretations about specific regions involved, these are restricted to smaller or less-resolved networks (Kiebel et al., 2009). To
gain insight into the local features of structural network architecture
that support functional interactions related to this task, we utilized
diffusion and T1-weighted MRI data to understand intersubject differences in structural connectivity.
Magnetic resonance imaging data
Preprocessing of the diffusion weighted images was performed with
algorithms included in the TORTOISE software package (www.tortoi
sedti.org) (Pierpaoli et al., 2010). Diffusion weighted images were first
corrected for motion and eddy current distortions according to Rohde
et al. (2004) including proper reorientation of the b-matrix to account
for the rotational component of the subject rigid body motion
(Leemans and Jones, 2009). In addition, B0 susceptibility induced
echo planar image distortions were corrected using an image registration based approach using B-splines (Wu et al., 2008). All corrections
were performed in the native space of the diffusion weighted images.
For consistency, all images were reoriented into a common space
defined by the mid-sagittal plane, the anterior commissure and the
posterior commissure (Bazin et al., 2007) also with appropriate rotations to the b-matrix. A non-linear diffusion tensor model was then fit
to the corrected data. Tensors volumes for Patients SP3 and SP5, who
had right-hemispheric lesions, were then right-left flipped, with appropriated reflections of the tensors applied as well.
Following tensor estimation, spatial normalization was performed
using a non-parametric, diffeomorphic deformable image registration
technique implemented in DTI-TK (www.nitrc.org/projects/dtitk/),
which incrementally estimates its displacement field using a
Parietofrontal integrity after stroke
tensor-based registration formulation (Zhang et al., 2006). It is designed to take advantage of similarity measures comparing tensors
as a whole via explicit optimization of tensor reorientation and includes
appropriate reorientation of the tensors following deformation.
Lesion segmentation
Lesions in each patient were segmented using an iterative, partially
unsupervised method. First, T2-weighted volumes were rigid-body
aligned with T1-weighted volumes. These two aligned volumes were
then used as multi-channel inputs to the FMRIB Automated
Segmentation Tool (FAST), a part of the FMRIB Software Library
(http://www.fmrib.ox.ac.uk/fsl/). FAST was used to derive partial
volume estimates at each voxel for grey matter, white matter and
CSF tissue classes. These partial volume estimates were then
non-linearly transformed into MNI152 space using FMRIB’s
Non-linear Image Registration Tool, and compared to a standard
map of partial volume estimates derived from the same scans (identical
acquisition sequences) acquired in 120 healthy volunteers on the same
scanner through the computation of a distance map (measured as the
Euclidean distance between patients with stroke and healthy volunteer
template grey matter, white matter, and CSF partial volume estimate
vectors at each voxel location). The resulting distance map was thresholded at 0.95, and binarized to create a lesion mask in MNI space. An
MNI-space ventricular mask was then used to remove any part of the
lesion mask that included portions of the ventricles. The resulting
lesion mask was then eroded, dilated and smoothed with a 1-mm
radius spherical kernel, and transformed back into the original subject
space using the inverse non-linear warp field. The mask was then used
as an input mask for subsequent non-linear registration iterations. For
each iteration, the inclusion of the previous lesion mask resulted in a
subsequent change in the non-linear registration to the MNI template.
A total of 10 iterations were performed in this manner. The final lesion
masks were then visually inspected, and any artefacts present were
manually corrected. A secondary segmentation of the total lesion was
implemented to distinguish between core and peri-necrotic regions
based on differences in T1 signal intensity.
Structural network construction
We used the Johns Hopkins University Probabilistic Fibre Atlas (http://
cmrm.med.jhmi.edu) (Zhang et al., 2010) in conjunction with the segmented individual patient lesions to derive a structural connectivity
matrix, and construct a weighted, undirected structural network
graph. Regions of interest used as seeds and targets in the construction of the atlas, were employed as nodes in the anatomical network.
Probabilistic atlas tracts were non-linearly transformed to MNI152
standard space with FMRIB’s Non-linear Image Registration Tool,
and used to define connections between each node based on the
tractography-based connectivity matrix published by Zhang et al.
(2010). For each patient, lesion segmentation masks were also
non-linearly transformed into MNI152 standard space, with all tract
values for voxels overlapping with the lesion mask in a particular subject set to zero. As the probability value for each tract falls off at
voxels distant from its centre, this meant that lesion overlap with central regions of each tract were weighted more heavily in terms of the
impact on structural connectivity. The weight for a given network
connection was defined as the ratio of the spared fibre tract probability
sum to the total original tract probability sum.
The resulting connectivity matrix was then subjected to one additional correction procedure. Since it is probable that the net effect of a
core lesion that transects a fibre tract is greater than an effect caused
by a reduction in tract volume, a custom cluster search algorithm was
used to identify non-contiguous components of the tract indicative of
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transections following removal of tract voxels overlapping with the
core lesion segmentation. If multiple components were present, with
at least two of them possessing a volume 520% of the total tract
volume, the transection was considered to be significant, and the connectivity value was set to zero. This threshold was used to eliminate
erroneous or relatively minor transections that occurred along the edge
of the tracts where measurement noise and normal population variance make the identification of transections less reliable. The specific
use of 20% was empirically determined, as it provided the best correspondence with tract transections visually identified in one of the
patient data sets. Based on these criteria, a total of 13 tract transections were identified in our patient group, with at least one significant
transection defined in five of eight patients.
We decided against using a tractography-based approach to define
our structural network as peri-infarct regions usually contain high
amounts of glial cell aggregation that significantly affect water diffusion anisotropy in a complex manner (Budde and Frank, 2010). Under
these circumstances, the relative contribution of white matter microstructure properties and gliosis to between-subject differences in measured anisotropy cannot be fully dissociated with standard diffusion
MRI techniques (Newton et al., 2006; Kunimatsu et al., 2007). To
avoid this confound, we implemented lesion segmentation overlaps
with a probabilistic atlas of major white matter fibre bundles derived
from data acquired in healthy volunteers and gain insight into the
disruption of structural connectivity within the ipsilesional hemisphere
(Riley et al., 2011).
Measures of nodal and edge betweenness centrality, which describe
the degree to which individual brain regions and white matter fibres
respectively contribute to the shortest pathway connecting other brain
regions, were calculated for each patient’s weighted structural connectivity matrix. The nodal betweenness centrality of a given node n
is defined as:
CB ðnÞ ¼
X ij ðnÞ
ij
i6¼n6¼j
where ij is the total number of shortest paths from node i to node j
and ij(n) is the number of those shortest paths that pass through
node n. The edge betweenness centrality of a given node e is defined
as:
CB ðeÞ ¼
X ij ðeÞ
i6¼j
ij
where ij is the total number of shortest paths from node i to node j
and ij(n) is the number of those shortest paths that contain edge e.
Tract-based spatial statistics analysis of extralesional
fractional anisotropy
To explore structural network characteristics in the contralesional
hemisphere, we used a complimentary tract-based spatial statistics
analysis approach that characterizes white matter microstructure relationships associated with sensorimotor rhythm modulation skill in
voxels not contributing to the lesion segmentation in any patient.
Fractional anisotropy maps for each patient were created from the
spatially normalized tensor outputs from DTI-TK. A mean fractional
anisotropy image was created and then skeletonized using a fractional
anisotropy threshold of 0.2. Each subject’s aligned fractional anisotropy image was then projected onto this mean skeleton by searching
perpendicular from the skeleton for maximum fractional anisotropy
values. This step allows for the statistical comparison of fractional anisotropy values from homologous regions of the fractional anisotropy
map. An extralesional group mask was constructed by summing
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individual patient lesion masks transformed into the common group
tensor space, binarizing, and then inverting. Thus, only voxels falling
outside the lesioned mask boundaries in all patients were included in
subsequent analyses. Between-subject variance in fractional anisotropy
from homologous regions was then related to voluntary modulation of
sensorimotor rhythm, with the acquired skill serving as a general linear
model regressor (Buch et al., 2010). The threshold free cluster enhancement option, a novel method for enhancing cluster-like structures in statistical images, was used for subsequent statistical tests.
Resulting corrected P-value maps were thresholded at P 4 0.05.
Average fractional anisotropy values within clusters were then correlated with the sensorimotor rhythm modulation skill to determine the
correlation coefficient for the cluster.
Voxel-based morphometry region of interest analysis
of extralesional grey matter volume
We extended our analysis to grey matter regions adjacent to the white
matter voxels where fractional anisotropy correlated with sensorimotor
rhythm modulation skill. A voxel-based morphometry region of interest analysis was performed using the standard FMRIB Software Library
pipeline. First, structural images were brain-extracted using BET. Next,
tissue-type segmentation was carried out using FAST. The resulting
grey matter partial volume estimates were then aligned to MNI152
standard space with FMRIB’s Non-linear Image Registration Tool. The
resulting images were averaged to create a study-specific template, to
which the native grey matter images were then non-linearly
re-registered. The registered partial volume images were then corrected for local expansion or contraction by dividing by the Jacobian
of the warp field. The corrected segmented images were then
smoothed with an isotropic Gaussian kernel with a sigma of 4 mm.
Two separate spherical region of interest masks with a radius of 25 mm
were centred on the MNI-space location of the maximum t-statistics
for the two clusters identified in the tract-based spatial statistics analysis relating white matter fractional anisotropy with skill (x, y,
z = 29, 56, 35 and 38, 21, 32). All voxels of both masks
fell within extralesional space. Finally, a voxel-wise general linear
model was applied using permutation-based non-parametric testing.
Resulting corrected P-value maps were thresholded at P 4 0.05.
Average grey matter volume values within clusters were then correlated with the sensorimotor rhythm modulation skill to determine the
correlation coefficient for the cluster.
This study utilized the high-performance computational capabilities
of the Biowulf Linux cluster at the National Institutes of Health,
Bethesda (http://biowulf.nih.gov). Metrics for both functional and
structural network graphs were computed using the Brain
Connectivity Toolbox (Rubinov and Sporns, 2010).
Results
Patient lesion characteristics
All patients displayed single unilateral lesions that differed in
volume and location (Fig. 1A). There were some shared features
however, including a disproportionate disruption of white matter
(74–95% of lesion volume; Table 1) predominantly occurring in
the superior portion of the corona radiata and the external medullary lamina (Fig. 1B). In terms of specific white matter fibre
tracts, the infarcted brain tissue overlapped with the corticospinal
tract and thalamic radiations, consistent with previous reports that
E. R. Buch et al.
lesions of these tracts result in severe hand motor deficits (Newton
et al., 2006; Ward et al., 2006).
Volitional control of sensorimotor
rhythm skill
Despite an overall group improvement in skill [t(7) = 3.77;
P 5 0.01], there was substantial variability in the ability of different patients to improve their success rate in volitional control of
sensorimotor rhythm and consequently brain–computer interface
induced hand grasping function (Fig. 3A). Seven of eight patients
showed a significant increase in skill (detection of significant
change-point for = 0.05), with a majority of patients showing
exponential performance increases during training with variable
growth rates and delays of onset. Patient SP01 was the single
patient who did not show a significant increase in skill with
training.
Functional network: global cost
efficiency
Global functional network cost efficiency, which quantifies the
overall network capacity to integrate information relative to the
number of significant functional interactions between nodes,
showed a significant positive relationship with acquired sensorimotor rhythm modulation skill [ = 6.54, t(7) = 3.08, P 5 0.05;
Fig. 3C].
Average task-related sensorimotor rhythm power modulation
after training also showed a significant positive relationship with
final success rate [ = 1.07, t(7) = 2.91, P 5 0.05; Fig. 3B]. This
was expected as performance was directly related to a transformation of the sensorimotor rhythm modulation state. Thus, patients
who were able to produce more separable sensorimotor rhythm
power states, through greater event-related synchronization or
desynchronization generated by grasping imagery, acquired
greater sensorimotor rhythm modulation skill.
Ipsilesional structural network: nodal
and edge betweenness centrality
Figure 4 displays the average lesion damage to the tracts comprising the structural network. As would be expected for the severe
hand motor deficits observed in this patient group, overlap of
segmented patient lesions with partial volume estimates of grey
and white matter determined from a large cohort of healthy volunteers showed on average the most significant damage was to
the ipsilesional corticospinal tract. There was also significant
damage to the thalamocortical radiations, particularly those connecting thalamic nuclei with the ipsilesional pre- and post-central
gyrus, and the ipsilesional superior and middle frontal gyrus.
Importantly, the data identified interindividual differences in
damage to short and long association fibre pathways connecting
premotor, primary sensorimotor and posterior parietal regions of
the network.
Figure 5 shows the relationship between two measures of network integration, nodal and edge betweenness centrality, with
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Figure 3 Skill, task-related sensorimotor rhythm power contrast, and global functional network cost-efficiency. (A) Sensorimotor rhythm
(SMR) modulation skill. Skill was defined as the proportion of correct trials for each training session, and showed a significant increase for
the group [t(7) = 3.77; P 5 0.01]. Group mean (thick black line) is shown with the 95% confidence intervals (shaded grey area). Individual
subject curves (thin black lines) are overlaid to illustrate the variability across the group. (B) Task-related sensorimotor rhythm power
contrast between target conditions showed a significant positive relationship with final success rate [ = 1.07, t(7) = 2.91, P 5 0.05].
(C) Global cost-efficiency across the entire magnetoencephalography array shows a significant positive relationship with acquired
sensorimotor rhythm modulation skill [ = 6.54, t(7) = 3.08, P 5 0.05].
skill. Network damage induced a complex pattern of changes in the
relative influence of ipsilesional hemisphere nodes and ipsilesional
association fibre and interhemispheric edges on global network integration. On the other hand, the influence of contralesional nodes
and edges displayed a uniform relative increase, as damage in
the opposite hemisphere enhanced their role in integrating information between frontal and parietal regions of the brain.
Only two significant relationships were observed between local
structural network properties and sensorimotor rhythm modulation
skill. First, nodal betweenness centrality measured at the ipsilesional
precentral gyrus was significantly related to both sensorimotor
rhythm modulation skill [ = 0.003, t(7) = 3.42, P 5 0.05; Fig. 5B],
and task-related sensorimotor rhythm modulation range [ = 0.002,
t(7) = 4.11, P 5 0.01; Fig. 5B]. Furthermore, edge betweenness
measured for the pathway connecting the ipsilesional angular
gyrus and middle frontal gyrus, a component of the frontoparietal
portion of the superior longitudinal fascicle, also showed a significant
positive relationship with acquired sensorimotor rhythm modulation
skill [ = 0.041, t(7) = 5.29, P 5 0.005; Fig. 5D].
Extralesional structural network: white-matter
microstructure integrity and grey matter volume
Again, we took the conservative approach of completely excluding
voxels contributing to the lesion mask in any patient from our
analyses exploring the voxel-wise relationships between white
matter anisotropy or grey matter density and sensorimotor
rhythm modulation skill. Lesion pathology may violate some assumptions relating these measures to anatomical microstructure in
the brain, leading to findings that are difficult to interpret. The
results of the tract-based spatial statistics analysis relating extralesional fractional anisotropy to sensorimotor rhythm modulation
skill revealed two significantly correlated clusters in the contralesional superior longitudinal fascicle. The first cluster encompassed
a caudal region of the contralesional superior longitudinal fascicle
adjacent to the anterior intraparietal sulcus (cluster size = 105; max
t-stat = 7.94; MNI location = 29, 56, 35; Fig. 6A and B). The
second cluster was located in a central region of the contralesional
superior longitudinal fascicle, underlying the contralesional sensorimotor cortex (cluster size = 96; max t-stat = 12.37; MNI location = 38, 21, 32; Fig. 6C and D).
A voxel-based morphometry–region of interest analysis revealed
two clusters of grey matter, co-localized to the white matter regions identified with the tract-based spatial statistics analysis,
which showed a significant correlation between volume and sensorimotor rhythm modulation skill. The more caudal cluster was
located within the contralesional intraparietal sulcus (cluster volume = 440 mm3; max t-stat = 4.20; MNI location = 28, 60,
50; Fig. 7A and B). The second cluster was located along the
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E. R. Buch et al.
Figure 4 Mean impact of lesions on structural connectivity patterns. Fifty corticospinal, thalamocortical, short and long association fibre,
and transcallosal tracts and their 36 related seed and target regions of interest from the JHU Probabilistic Fibre Atlas (Zhang et al., 2010),
were used to construct weighted, undirected structural network connectivity matrices for the ipsilesional hemisphere in each patient.
(A) Cumulative mean fibre damage for all tracts connected to a network node (red filled circles). Individual patient values are also
displayed (small grey filled circles). Diameter of the red-filled circles is proportional to the group SD for each node, relevant to the
evaluation of interindividual differences in skill. (B) Group mean symmetric network matrix showing the mean fibre tract damage for
each edge (1—connectivity value). Filled circles located at grid-line intersections represent existing edges (connections) in the network.
Rows and columns with the white background (dividing the rest of the matrix into quadrants) contain edges representing corticospinal and
thalamocortical fibre pathways. Edges representing ipsilesional hemisphere short and long association fibres are located in the top-left
quadrant (with the black background). Transcallosal fibre pathway edges are located in lower-left and upper-right quadrants.
Contralesional hemisphere short and long association fibre edges between contralesional nodes directly connected to ipsilesional nodes
through transcallosal pathways are located in the lower-right quadrant. Circle diameter is proportional to the SD of lesion damage to a
particular tract across patients. Large diameter circles highlight edges with relatively high damage variability across the group. Circle
colours reflect the group mean fibre tract damage. Grey coloured circles represent edges that are undamaged in all patients (see colour bar
scale). Note the most severe damage occurs in the corticospinal tract as indicated by bright yellow circles. Additionally, there is moderate
damage to ipsilesional short and long association fibre tracts connecting premotor, sensorimotor and posterior parietal regions as shown in
large magenta circles. AG = angular gyrus; CingG = cingulate gyrus; CL = contralateral; CP = cerebral peduncle; Cu = cuneus;
Fu = fusiform gyrus; IFG = inferior frontal gyrus; IL = ipsilateral; IOG = inferior occipital gyrus; LFOG = lateral fronto-orbital gyrus;
LG = lingual gyrus; MFG = middle frontal gyrus; MFOG = middle fronto-orbital gyrus; MOG = middle occipital gyrus; MTG = middle
temporal gyrus; PoCG = post-central gyrus; PrCG = precentral gyrus; PrCu = pre-cuneus; RG = rectus gyrus; SFG = superior frontal gyrus;
SMG = supramarginal gyrus; SOG = superior occipital gyrus; SPG = superior parietal gyrus; STG = superior temporal gyrus; TH = thalamus.
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Figure 5 Structural network nodal and edge betweenness centrality and skill. Nodal betweenness centrality characterizes the influence of
a single node over the sharing of information between other nodes. (A) Grey bars show nodal betweenness centrality values for each node
in an intact network prior to the inclusion of lesions (Zhang et al., 2010). Red circles show the group mean betweenness centrality for each
network node following lesion inclusion. Circle diameter is proportional to the SD of nodal betweenness centrality across patients. Small
grey filled circles show individual patient values. (B) Lesion-induced changes in precentral gyrus nodal betweenness centrality correlates
with both skill [ = 0.003, t(7) = 3.42, P 5 0.05], and task-related sensorimotor rhythm power contrast between target conditions
[ = 0.002, t(7) = 4.11, P 5 0.01]. (C) Group mean symmetric network matrix showing the mean lesion-induced change in betweenness
centrality for each edge following application of lesion damage to the network. As in Fig. 4, filled circles located at grid-line intersections
represent existing edges (connections) in the network. Rows and columns with the white background (dividing the rest of the matrix into
quadrants) contain edges representing corticospinal and thalamocortical fibre pathways. Edges representing ipsilesional hemisphere short
and long association fibres are located in the top-left quadrant (with the black background). Transcallosal fibre pathway edges are located
in lower-left and upper-right quadrants. Contralesional hemisphere short and long association fibre edges between contralesional nodes
directly connected to ipsilesional nodes through transcallosal pathways are located in the lower-right quadrant. Circle diameter is proportional to the SD in edge betweenness centrality difference across patients. Large diameter circles highlight edges with relatively high
betweenness centrality variability across the group. Circle colours reflect the group mean edge betweenness centrality difference. Grey
coloured circles indicate edges where betweenness centrality did not change (see colour bar scale). (D) Change in edge betweenness
centrality measured for the connection between the ipsilesional angular gyrus and middle frontal gyrus shows a significant positive
relationship with skill [ = 0.041, t(7) = 5.29, P 5 0.005]. It should be noted that damage caused by lesions (Fig. 4) induces a complex
pattern of changes in the relative influence of ipsilesional hemisphere nodes and ipsilesional association fibre and interhemispheric edges
on global network integration. However, the influence of contralesional nodes and edges displays a uniform relative increase, as damage in
the opposite hemisphere enhances their role in integrating information between frontal and parietal regions of the brain. AG = angular
gyrus; CingG = cingulate gyrus; CL = contralateral; CP = cerebral peduncle; Cu = cuneus; Fu = fusiform gyrus; IFG = inferior frontal gyrus;
IL = ipsilateral; IOG = inferior occipital gyrus; LFOG = lateral fronto-orbital gyrus; LG = lingual gyrus; MFG = middle frontal gyrus;
MFOG = medial fronto-orbital gyrus; MOG = middle occipital gyrus; MTG = middle temporal gyrus; PoCG = post-central gyrus;
PrCG = precentral gyrus; PrCu = pre-cuneus; RG = rectus gyrus; SFG = superior frontal gyrus; SMG = supramarginal gyrus;
SMR = sensorimotor rhythm; SOG = superior occipital gyrus; SPG = superior parietal gyrus; STG = superior temporal gyrus;
TH = thalamus.
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Figure 6 Extralesional white matter fractional anisotropy (FA) related to skill. Two white matter clusters in the contralesional hemisphere
were the only locations where there was a significant positive relationship between extralesional fractional anisotropy and acquired
sensorimotor rhythm modulation skill across the stroke patient group. (A and B) A caudal region of the contralesional superior longitudinal
fascicle adjacent to the anterior intra-parietal sulcus (cluster size = 105; max t-stat = 7.94; MNI location = 29, 56, 35). (C and D) A
central region of the contralesional superior longitudinal fascicle underlying sensorimotor cortex (cluster size = 96; max t-stat = 12.37; MNI
location = 38, 21, 32). Both clusters are significant at the level of P 5 0.05 (corrected). Note that all lesioned areas were excluded
from this analysis.
grey–white matter border of the rostral precentral gyrus near the
junction of rostral M1, ventral premotor cortex and the dorsal
premotor cortex (cluster volume = 248 mm3; max t-stat = 3.87;
MNI location = 42, 10, 46; Fig. 7C and D) (Tomassini et al.,
2007).
Discussion
The purpose of this study was to gain insight into the mechanisms
related to volitional control of neural activity through operant conditioning in patients with chronic stroke. This skill is important for
acquiring control of computer-based applications or mechanical
devices that can replace or facilitate extinguished behaviours, as
in the case of brain–machine interfaces (Birbaumer and Cohen,
2007; Wolpaw, 2007). Additionally, voluntary control of brain
rhythms could promote functional plasticity following stroke and
traumatic brain injury that impact recovery of motor skills (Ang
et al., 2010; Wang et al., 2010b; Grosse-Wentrup et al., 2011a).
To address this question, we longitudinally trained a group of patients with chronic stroke with severe paralysis of the affected
hand to volitionally modulate neural activity recorded from sensorimotor regions of the lesioned hemisphere, and used a comprehensive analysis of structural and functional network characteristics
to evaluate possible substrates of this skill. Our results demonstrate that patients with more cost efficient global functional network activity and greater bi-hemispheric indices of structural
connectivity between frontal and parietal regions exerted better
sensorimotor rhythm modulation skill after longitudinal training
associated with grasping imagery.
We purposefully chose a group of patients with chronic stroke
with comparably severe hand motor deficits, who were unable to
elicit extrinsic hand muscle EMG activity. Due to the severity of
the motor deficits, this is a population of patients who are typically
excluded from most experimental studies (Page et al., 2007) and
stand to benefit from training strategies that involve motor imagery (Sharma et al., 2006; Page et al., 2009) and neurofeedback
(Birbaumer and Cohen, 2007; Fetz, 2007; Wolpaw, 2007;
Donoghue, 2008; Birbaumer et al., 2009). A further rationale
for studying this patient group is that they present an ideal opportunity to study volitional control of neural activity in the lesioned brain without the confound of uncontrolled voluntary
paretic hand muscle contraction. These patients were uniformly
required to engage motor networks in an experiential and functionally relevant manner through grasping imagery of the affected
hand, resulting in corresponding actions of a hand orthosis in
which the hand was embedded. Additionally, we were interested
in including patients whose comparable clinical outcome in the
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Figure 7 Extralesional grey matter (GM) volume related to skill. A region of interest-based voxel-based morphometry analysis of grey
matter regions co-localized to regions of fractional anisotropy that correlated with sensorimotor rhythm modulation skill revealed two
complimentary clusters showing a positive correlation between grey matter volume and sensorimotor rhythm modulation skill. (A and B) A
caudal cluster is located within the contralesional intraparietal sulcus (cluster volume = 440 mm3; max t-stat = 4.20; MNI location = 28,
60, 50). (C and D) A second cluster is located along the grey–white matter border of the rostral precentral gyrus and near the junction of
rostral M1, ventral premotor cortex and the dorsal premotor cortex (cluster volume = 248 mm3; max t-stat = 3.87; MNI location = 42,
10, 46). Both clusters are significant at the level of P 5 0.05 (corrected).
chronic stage resulted from heterogeneous lesion pathology, thus
allowing us to explore the relationship between inter-individual
variability in indices of structural connectivity and sensorimotor
rhythm modulation skill.
Global functional network
cost-efficiency
We found that voluntary control of neural activity in the form of
sensorimotor rhythm modulation skill following longitudinal training correlated with global but not local cost-efficiency, suggesting
a link between skill and distributed functional network architecture. Global functional network cost-efficiency appears to play an
important role in other settings as well. For example, functional
network cost-efficiency appears to predict working memory capacity (Bassett et al., 2009). Furthermore, longitudinal resting-state
functional connectivity MRI data acquired throughout the
acute-to-chronic stages of stroke suggest that changes in connectivity resulting in increased network efficiency is indicative of adaptive reorganization that results in improved recovery of motor
function (Wang et al., 2010a; Grefkes and Fink, 2011; Rehme
et al., 2011b). Our results are consistent with these previous reports and suggest that global functional network cost-efficiency
may represent a more powerful predictor of acquired skill than
spatially-dependent (i.e. voxel-based) measures, as lesion heterogeneity is highly prevalent after chronic stroke (Crofts et al.,
2011). Changes in connectivity measurements between early
and late training did not predict sensorimotor rhythm modulation
skill, probably due to the high inter and intrasubject variability in
the initial sessions.
Our primary interest was in utilizing sensorimotor rhythm
modulation that occurs during normal motor imagery to drive
the hand orthosis in our patients with chronic stroke. Each patient
had an initial session during which they performed alternate blocks
of rest and motor imagery of the affected hand to characterize
individual spatial and frequency profiles of sensorimotor rhythm
modulation. We found that the frequency band range
producing most robust modulation across our patient group, in
terms of both amplitude and topographic characteristics, was
9–12 Hz of the alpha band. This was consistent with a previous
study performed in healthy volunteers that also used a
magnetoencephalography-based brain–computer interface with
the same type of classifier (Mellinger et al., 2007), as well as in
a stroke patient study using an EEG-based brain–computer interface (Prasad et al., 2010). Our interest in looking at functional
connectivity across the magnetoencephalography array was to
assess whether global patterns of synchronization between regions
were related in some way to regional sensorimotor rhythm modulation skill. Several studies looking at the role of synchronous
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oscillatory activity suggest that alpha band rhythms play an important role in the coupling of distant brain regions allowing for
global integration of network information (Palva and Palva, 2011).
The measure of network organization we employed here,
cost-efficiency, was used to assess this role of long-range alpha
band synchronization, as it estimates the capacity of the functional
network configuration to integrate global information.
Voluntary control of neural activity
relates to indices of parietofrontal
structural connectivity
We then evaluated the relationship between skill and interpatient
differences in network architecture caused by lesion-related decrements of structural connectivity. First, we found that volitional
control of neural activity at the end of training correlated with
nodal betweenness centrality of the ipsilesional precentral gyrus.
In isolation, the relationship between skill and this property of
ipsilesional structural connectivity is consistent with damage to
descending corticospinal or corticothalamic pathways typically
observed in patients with severe motor deficits (Newton et al.,
2006; Ward et al., 2006). However, as the precentral gyrus is
directly connected to premotor (superior and middle frontal gyri)
and posterior parietal (angular and supramarginal gyri) regions in
the network that serve as important hubs for occipitoparietal and
prefrontal regions, damage to these pathways will also reduce
betweenness centrality of the precentral gyrus. This is consistent
with functional connectivity MRI findings reported by Wang et al.
(2010a, 2011), showing that a reduction in functional connectivity
between M1 and premotor regions resulted in a decrease in nodal
betweenness centrality.
Additionally, skill correlated with edge betweenness centrality of
the frontoparietal portion of the superior longitudinal fascicle. In
the structural network model, ipsilesional precentral gyrus and
frontoparietal portion of the superior longitudinal fascicle are the
primary means through which information is shared between
visuomotor regions located in posterior parietal and frontal cortices
(Makris et al., 2005; Schmahmann et al., 2007). The frontoparietal portion of the superior longitudinal fascicle is a long association fibre pathway that directly connects the precentral gyrus and
middle frontal gyrus with the angular gyrus and supramarginal
gyrus. The connectivity of the frontoparietal portion of the superior longitudinal fascicle appears consistent with two subcomponents of the superior longitudinal fascicle, superior longitudinal
fascicle II and III, which have been previously defined in both
humans and monkeys (Makris et al., 2005; Schmahmann et al.,
2007). The ipsilesional precentral gyrus is also directly connected
to the inferior and superior frontal gyri and the post-central gyrus
in the ipsilesional hemisphere through short association fibre pathways. This is consistent with the known anatomical connectivity of
distal forelimb representations located within the primary motor,
somatosensory, and premotor regions in the monkey (Dum and
Strick, 2005). It also connects to the contralesional precentral
gyrus through transcallosal fibres and is the only cortical region
in the model that contributes to the corticospinal tract.
E. R. Buch et al.
The results of the tract-based spatial statistics and region of
interest-based voxel-based morphometry analyses, which did exclude peri-infarct areas and thus a large portion of the ipsilesional
hemisphere, revealed consistent homologous structures in the contralesional hemisphere where differences in microstructure correlated with skill. These observations suggest that grey matter
volume in the intraparietal sulcus and a region near the transition
between rostral primary motor and caudodorsal ventral premotor,
as well as microstructural integrity of the superior longitudinal fascicle, a fibre bundle connecting these two regions, are directly
related to skill. Thus, white and grey matter architectural features
may provide support to the increased global integration of information by contralesional white matter association fibres linking
frontal and parietal regions of the cortex. It is well known that
the contralesional hemisphere contributes to functional recovery
after brain lesions that result in more substantive motor deficits
(Johansen-Berg et al., 2002; Harris-Love et al., 2005, 2011;
Rehme et al., 2011a). It is conceivable that testing of patients
with lesser motor deficits, known to benefit from ipsilesional activity, could unveil a stronger ipsilesional contribution as proposed
before (Ward et al., 2003; Fridman et al., 2004). Additionally, it
remains to be determined if differences in network architecture
identified in this study reflect pre-morbid state or compensatory
changes. Direct comparison between the neural substrates underlying performance of this task in patients and healthy subjects,
which could contribute to address this issue, is problematic because the rate of skill acquisition in patients is very slow (weeks)
while in healthy volunteers is very fast (often within a single session) (Mellinger et al., 2007).
The superior longitudinal fascicles II and III identified in our
study are the primary white matter fibre pathways that directly
connect parietofrontal regions that are functionally related to the
planning, selection and execution of reaching and grasping movements in both monkeys and humans (Desmurget et al., 1999;
Shimazu et al., 2004; Cattaneo et al., 2005; Tunik et al., 2005,
2008; Grol et al., 2007; Olivier et al., 2007; Umilta et al., 2007;
Davare et al., 2010), with a majority of the interactions within this
reaching and grasping network showing similar modulation with
related imagery as well (Gao et al., 2010). Our data provide insight into how these intact networks reorganize to acquire skill
after human brain lesions. In our patients, skill also correlated
with fractional anisotropy in the contralesional superior longitudinal fascicle and with grey matter volume in the adjacent intraparietal sulcus and rostral precentral gyrus. This implies that a
patient’s ability to volitionally modulate neural activity within the
ipsilesional sensorimotor cortex after longitudinal training may be
related to the recruitment of a more integrated bilateral parietofrontal functional network than those described in healthy human
and non-human primates in the literature. Reaching and grasping
behaviour, as that asked of our patients to imagine, is the primary
means through which humans and other primates perform
goal-directed actions. Several computational and theoretical
models have been proposed that attempt to account for the contributions of specific frontal and parietal cortical regions to these
behaviours (Wolpert, 1997; Wolpert and Ghahramani, 2000;
Shadmehr and Wise, 2005; Frey et al., 2011). While dorsolateral
and orbital prefrontal regions, which integrate information related
Parietofrontal integrity after stroke
Brain 2012: 135; 596–614
| 611
to attention and motivation, help identify goals (Price et al.,
1996), interactions between granular prefrontal regions with
dorsal and ventral premotor areas lead to the selection of appropriate actions for achieving these desired goals (O’Shea et al.,
2007; Tunik et al., 2008; Buch et al., 2010). On the other
hand, the superior and inferior posterior parietal cortex is responsible for integrating visual and proprioceptive information used to
evaluate the current state of the grasping action relative to the
goal while it is being executed (Glover et al., 2005; Tunik et al.,
2005). This evaluation is continuously relayed to the premotor
cortex, which uses the information to update action plans until
the goal is achieved (Tunik et al., 2008; Davare et al., 2010).
(Bai et al., 2008), gamma (Grosse-Wentrup et al., 2011b) and
slow cortical oscillations (Hinterberger et al., 2005), not studied
in this investigation. Additionally, a larger population of patients
with more heterogeneous lesions and deficits, as well as longitudinal imaging data helping to characterize changes in structural
networks over time will be required before prognostic benchmarks
can be identified. Indeed, the continued connectomics-based exploration of structural and functional brain networks at the systems level may provide greater insight into relationships between
behaviour, plasticity and brain anatomy and physiology following
stroke (Schmahmann and Pandya, 2008; Bullmore and Sporns,
2009; Rubinov and Sporns, 2010; Bullmore and Bassett, 2011).
Voluntary modulation of neural activity
associated with grasping imagery
training after stroke
Funding
Together, these structural and functional findings support a coherent argument for the superior longitudinal fascicle, the source of
long-range corticocortical pathways connecting parietofrontal
visuomotor areas, as a crucial anatomical substrate underlying
the ability to volitionally modulate neural activity within ipsilesional
sensorimotor areas following stroke. Over the past decade, it has
been shown that it is possible to elicit voluntary modulation of
sensorimotor rhythm through motor imagery (McFarland et al.,
2000; Pfurtscheller, 2000). Studies in healthy volunteers and patients with stroke show that motor imagery results in similar parietofrontal functional network interactions to those observed for
execution of hand motor tasks (Sharma et al., 2009a; Gao et al.,
2010). Moreover, the ability of healthy volunteers to acquire volitional control of sensorimotor rhythm modulation appears to be
related to the degree that the constituent regions of this network
are recruited by the motor imagery strategy used (Halder et al.,
2011). Thus, it would be reasonable to expect that volitional control of neural activity through operant conditioning using motor
imagery would relate to architectural features of this network.
Here, the present results suggest that the impact of individual
brain lesions on both local and global properties of the intrinsic
motor system can be characterized and give insight into the dynamic range of the network, even in a relatively small group of
patients possessing lesion heterogeneity, but homogenously severe
hand motor deficits.
It is possible that in the future, knowledge of individual patient’s
structural and functional network architecture may be used as a
biomarker predictive of response to neurorehabilitative treatments
(Cramer et al., 2007) or to optimize training duration (Page et al.,
2009), intensity (Wolf et al., 2007) or practice schedule (Wolf
et al., 2010). Clearly, more work will be required to reach this
point. We hope that this knowledge could eventually be used in
concert with non-invasive brain stimulation protocols (Hummel
et al., 2005; Boggio et al., 2007; Nowak et al., 2008; Celnik
et al., 2009; Edwards et al., 2009; Grefkes et al., 2010) or
pharmacological interventions (Chollet et al., 2011; Wang et al.,
2011) to sculpt plasticity in a manner that promotes the formation
of adaptive network solutions allowing for better volitional control
of sensorimotor or other brain rhythms, including beta
Intramural Research Program National Institute of Neurological
Disorders and Stroke Intramural Research Program.
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